T^(h$dZddlmZmZddlZddlmZmZddl m Z m Z m Z ddl mZmZmZmZmZmZmZmZddlmZmZmZej4eZGd d eZd gZy) z"Image processor class for Swin2SR.)OptionalUnionN)BaseImageProcessor BatchFeature)get_image_sizepadto_channel_dimension_format)ChannelDimension ImageInputinfer_channel_dimension_formatis_scaled_imagemake_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargsloggingcFeZdZdZdgZ ddedeeefdededdf fd Z dd e jd ed e ee efd e ee effdZedddddej"dfdede ede ede ede ede ee efd ee efd e ee effdZxZS)Swin2SRImageProcessora Constructs a Swin2SR image processor. Args: do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. pixel_values do_rescalerescale_factordo_padpad_sizereturnNc \t|di|||_||_||_||_y)N)super__init__rrrr)selfrrrrkwargs __class__s r/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/swin2sr/image_processing_swin2sr.pyr!zSwin2SRImageProcessor.__init__6s2 "6"$,   imagesize data_formatinput_data_formatct||\}}||zdz|z|z }||zdz|z|z }t|d|fd|ffd||S)a Pad an image to make the height and width divisible by `size`. Args: image (`np.ndarray`): Image to pad. size (`int`): The size to make the height and width divisible by. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. input_data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. Returns: `np.ndarray`: The padded image. r symmetric)moder)r*)rr ) r"r'r(r)r* old_height old_width pad_height pad_widths r%r zSwin2SRImageProcessor.padEsq:!/u6G H I D(1,4zA $&*d2Y>  _q)n -#/   r&imagesreturn_tensorsc ||n |j}||n |j}||n |j}||n |j}t |}t |s t dt|||||D cgc] } t| }} |r#t|drtjd|t|d}|r!|D cgc]} |j| ||}} |r!|D cgc]} |j| ||}} |D cgc]} t| ||}} d|i} t!| | Scc} wcc} wcc} wcc} w) a Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image values between [0 - 1]. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_pad (`bool`, *optional*, defaults to `True`): Whether to pad the image to make the height and width divisible by `window_size`. pad_size (`int`, *optional*, defaults to 32): The size of the sliding window for the local attention. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of typ, input_data_format=input_data_formate `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - Unset: Use the channel dimension format of the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)rrrsize_divisibilityrzIt looks like you are trying to rescale already rescaled images. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.)r'scaler*)r(r*)input_channel_dimr)data tensor_type)rrrrrr ValueErrorrrrlogger warning_oncer rescaler r r) r"r3rrrrr4r)r*r'r9s r% preprocessz Swin2SRImageProcessor.preprocessns^$.#9Zt +9+E4K^K^!-4;;'38$V,F#:  &!)&  6<vay I  $ 5Rc dF gmn^cdhhu8GXhYnFnou ej '{N_ `  '>BB5= o s5D0D5'D:D?)Tgp?T)NN)__name__ __module__ __qualname____doc__model_input_namesboolrintfloatr!npndarrayrstrr r rFIRSTr rr? __classcell__)r$s@r%rr's (( ,3 ! !c5j) ! !  !  !&?CDH ' zz' ' eC)9$9:; ' $E#/?*?$@A ' R%&&**.!%"&;?4D4J4JDH\C\CTN\C! \C  \C 3- \C!sJ!78\C3 001\C$E#/?*?$@A\C'\Cr&r)rDtypingrrnumpyrIimage_processing_utilsrrimage_transformsrr r image_utilsr r r rrrrrutilsrrr get_loggerrAr<r__all__rr&r%rVsa)"FPP   JI   H %dC.dCN # #r&